Computational deconvolution to estimate cell type-specific gene expression from bulk data




Jaakkola Maria K., Elo Laura L.

PublisherOxford University Press

2021

NAR Genomics and Bioinformatics: Nucleic Acids Research Genomics and Bioinformatics

lqaa110

3

1

2631-9268

DOIhttps://doi.org/10.1093/nargab/lqaa110

https://academic.oup.com/nargab/article/3/1/lqaa110/6090161

https://research.utu.fi/converis/portal/detail/Publication/53628840



Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses different genes, or both tasks simultaneously. Among the two separate goals, the estimation of cell type proportions/abundances is widely studied, but less attention has been paid on defining the cell type-specific expression profiles. Here, we address this gap by introducing a novel method Rodeo and empirically evaluating it and the other available tools from multiple perspectives utilizing diverse datasets.


Last updated on 2024-26-11 at 22:43